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Infectious diseases remain a significant global health challenge, intensified by emerging pathogens, antimicrobial resistance, and limited healthcare access in low-resource settings whereas the emergence of Artificial Intelligence (AI) has transformed the way of infectious disease management by enhancing diagnostics, surveillance, drug discovery, and personalized treatment strategies. AI-driven approaches like Machine Learning (ML), Natural Language Processing (NLP) and deep learning, have facilitated early identification of disease, optimized healthcare resource allocation, and accelerated both the vaccine and drug development. AI powered diagnostic tools, such as computer vision-based medical imaging models and real-time epidemiological surveillance systems, have been instrumental in pandemic response efforts. Moreover, use of AI improved Anti-Microbial Resistance (AMR) monitoring, ensuring timely intervention against drug-resistant infections. More specifically, AI is developing at unprecedented scale which is being adopted and deployed even faster in every sphere of life globally. Despite its beneficial potential, there are some challenges like data privacy, ethical concerns, and infrastructure limitations causing barriers to widespread AI adoption in healthcare. Therefore, there is a requirement for collective global efforts to establish governance and standards that uphold the shared values, and address risks and build trust. Thus, the present review explores the current advancements, challenges, and future directions of AI in infectious disease management, highlighting its transformative impact on global health security.